West Java is in the five line on the list of provinces in Indonesia with the most COVID-19 cases, as Bandung Metropolitan Area (BMA) is the second most densely populated showing the highest number after Jakarta Greater Area. Bandung Metropolitan Area consist of Bandung City, Cimahi City, Bandung Regency, and West Bandung Regency. Then, an intense movement of people created between the connected city and regency. Bandung City became the epicenter of movement BMA, since it is the province capital city, business, and education center. This fact, putting BMA at the highest risk not only for the pandemic but also socioeconomic issues. The spatial time series risk forecasting information is an essential for the decision-maker to develop a day by day policy aimed for combating the COVID-19 pandemic issue. In this study, the pandemic risk is calculated by combining vulnerability, hazard, and geodemography information. Infimap provides the People in Pixels geodemographic data, added not only the exposure of population distribution to COVID-19 but also the ratio of age. Beside those data, the daily distribution of COVID-19 cases, network data, business point, health facility point, residentials area, geodemographic (People in Pixels), and daily COVID-19 Community Mobility Reports is also been used in this study. The daily vulnerability and hazard data created since the first case on March 4th until August 21st. The hazard area is create based on the expected travel area of positive COVID- 19 patient. While the vulnerability area is create using Spatial Multi Criteria Analysis (SMCA) of following data: service area of hospital, groceries (local market), and workspace. Further, the time series data of hazard and vulnerability area was inputted to develop the forecasting model based on the machine learning pipeline of Gaussian algorithm. As a result, this study shows the possibility to predict the future risk area of COVID-19 until the next 100 days condition, based on spatial timeseries forecasting model.
Pixel-based remote sensing (RS) is widely used for social science research in particular for geodemographic interpolation data studies i.e. population distribution, poverty mapping, etc. Basically, RS research needs adequate amount of ground-truth data either for developing, validating, or improving the accuracy of the RS model. The oldfashioned ground-truth collecting from the field is time-cost consuming notably if the area is large heterogeneity. Development of crowd-sourcing methods for geoinformation science (GIS) and RS is getting attention and become more popular, known as volunteered geographic information (VGI). VGI may offer time-cost efficiency technology for ground-truth collecting data. We use mobile phone-based survey123 for ArcGIS to collect the ground-truth data. These ground-truth data used for improving the geo-demographic estimation map. Firstly, the geo-demographic map was developed by the traditional dasymetric method. Then, Support Vector Machine algorithm is used for improving the accuracy of traditional dasymetric map by using several predictor variables from remote sensing dataset. DKI Jakarta Province was chosen as a case study. We compare the geo-demographic estimation model with the existing data from Central Statistics Bureau of Republic of Indonesia. Using the combination of crowdsourcing, VGI dataset and several remote sensing-based predictor variables, a new method of geodemographic estimation was produced with higher R2 of 0.66 compared with the traditional dasymetric method with R2 of 0.09.
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